A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse popul...

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Autores principales: Tatsuhiko Naito, Ken Suzuki, Jun Hirata, Yoichiro Kamatani, Koichi Matsuda, Tatsushi Toda, Yukinori Okada
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/d7317882897f4f4785597fe336ab7a96
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spelling oai:doaj.org-article:d7317882897f4f4785597fe336ab7a962021-12-02T13:31:14ZA deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes10.1038/s41467-021-21975-x2041-1723https://doaj.org/article/d7317882897f4f4785597fe336ab7a962021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21975-xhttps://doaj.org/toc/2041-1723Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.Tatsuhiko NaitoKen SuzukiJun HirataYoichiro KamataniKoichi MatsudaTatsushi TodaYukinori OkadaNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
description Human leukocyte antigen (HLA) genes contribute to risk of many complex traits, yet understanding inter-ethnic heterogeneity is computationally challenging. Here, the authors develop DEEP*HLA for imputation of HLA genotypes and show its ability to disentangle HLA variant risk effects in diverse populations.
format article
author Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
author_facet Tatsuhiko Naito
Ken Suzuki
Jun Hirata
Yoichiro Kamatani
Koichi Matsuda
Tatsushi Toda
Yukinori Okada
author_sort Tatsuhiko Naito
title A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_short A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_full A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_fullStr A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_full_unstemmed A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes
title_sort deep learning method for hla imputation and trans-ethnic mhc fine-mapping of type 1 diabetes
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/d7317882897f4f4785597fe336ab7a96
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